Row

Rank

Predicted Beta

Idiosyncratic Volatility

Row

Annualized return and volatility

Close
Annualized Return 0.1569
Annualized Std Dev 0.1778
Annualized Sharpe (Rf=0%) 0.8823

Row

Daily Return Statistics

Close
Observations 2875.0000
NAs 1.0000
Minimum -0.1154
Quartile 1 -0.0038
Median 0.0010
Arithmetic Mean 0.0006
Geometric Mean 0.0006
Quartile 3 0.0058
Maximum 0.0930
SE Mean 0.0002
LCL Mean (0.95) 0.0002
UCL Mean (0.95) 0.0011
Variance 0.0001
Stdev 0.0112
Skewness -0.4349
Kurtosis 11.5024

Downside Risk

Close
Semi Deviation 0.0082
Gain Deviation 0.0077
Loss Deviation 0.0092
Downside Deviation (MAR=210%) 0.0127
Downside Deviation (Rf=0%) 0.0080
Downside Deviation (0%) 0.0080
Maximum Drawdown 0.3076
Historical VaR (95%) -0.0170
Historical ES (95%) -0.0273
Modified VaR (95%) -0.0165
Modified ES (95%) -0.0309
From Trough To Depth Length To Trough Recovery
2020-02-20 2020-03-23 2020-06-09 -0.3076 77 23 54
2018-10-02 2018-12-24 2019-04-26 -0.2242 142 58 84
2011-07-25 2011-10-03 2012-01-25 -0.1606 128 50 78
2010-04-26 2010-07-02 2010-10-25 -0.1586 128 49 79
2015-12-02 2016-02-11 2016-07-12 -0.1262 153 49 104

Row

Monthly and Calendar Year Returns

Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec Close
2009 NA NA NA NA NA NA NA NA 0.1 -2.2 1.7 -0.4 -0.9
2010 1 0.7 0.3 -1.3 -1 -0.2 0 2.6 0.1 0.2 2.3 -0.3 4.5
2011 1.5 -1.5 0.3 0.2 -1.8 1.4 -0.2 -1 -1.8 -2.6 0.3 -0.2 -5.5
2012 0.6 0.5 0.1 0.3 -2.4 2.6 -0.4 0.5 0.2 1.1 0 1.8 4.9
2013 0.9 0.4 -0.5 -0.7 -1.4 0.8 1.1 -0.3 0.8 0.1 0.4 0.5 2.1
2014 -0.5 0.2 0.9 0 0.4 0.9 -0.4 0.2 -1.4 1 -1 -1 -0.7
2015 -1.1 -0.2 -0.4 1.2 0.4 0.9 0.1 -2.8 0.6 -0.5 1.1 -1.3 -2.1
2016 0.5 2.7 1.1 -0.4 0.1 0.5 0.4 0.1 0.6 -0.8 -0.9 -0.7 3.2
2017 0.3 1.4 0 0.4 0.6 0.3 0.3 0 0.5 0 -0.3 -0.4 3.1
2018 -0.3 -1.5 1.7 0.5 1.2 0.2 0.5 0.1 0.5 1.2 0.9 1.1 6.1
2019 -0.2 0.8 1.1 -0.6 -1.5 0.9 -0.4 -0.1 -0.9 0.8 -0.3 0.2 -0.2
2020 -1.7 0.2 -4.4 -2.6 0.5 1.5 1.9 1.4 1.1 -2.2 1.3 0.3 -2.7
2021 2.1 2.6 0.1 NA NA NA NA NA NA NA NA NA 4.9

Row

Price Chart

# tidytable [6 × 21]
  datadate   Close tic.x   spy   ret.x ret_1W.x ret_1M.x ret_3M.x ret_1Y.x ret_3Y.x ret_5Y.x tic.y   gld   ret.y ret_1W.y
  <date>     <dbl> <chr> <dbl>   <dbl>    <dbl>    <dbl>    <dbl>    <dbl>    <dbl>    <dbl> <chr> <dbl>   <dbl>    <dbl>
1 2009-09-28  25.0 SPY    106.  0.0179 -0.00120   0.0282    0.147  -0.120    -0.204  -0.04   GLD    97.0  0.0005  -0.0133
2 2009-09-30  24.6 SPY    106. -0.0039 -0.0056    0.0305    0.144  -0.0897   -0.210  -0.0559 GLD    98.8  0.0146   0.0002
3 2009-10-01  24.7 SPY    103  -0.0245 -0.0191    0.0279    0.147  -0.112    -0.229  -0.0784 GLD    97.9 -0.0097   0.0035
4 2009-10-02  24.3 SPY    102. -0.005  -0.0188    0.0267    0.141  -0.0837   -0.230  -0.0982 GLD    98.4  0.0049   0.0141
5 2009-10-07  25.0 SPY    106.  0.0027  0.002     0.0278    0.2     0.0577   -0.217  -0.0769 GLD   102.   0.0008   0.0355
6 2009-10-12  25.4 SPY    108.  0.0039  0.0352    0.0278    0.188   0.217    -0.204  -0.0468 GLD   104.   0.007    0.0375
# … with 6 more variables: ret_1M.y <dbl>, ret_3M.y <dbl>, ret_1Y.y <dbl>, ret_3Y.y <dbl>, ret_5Y.y <dbl>, rel <dbl>

Row

Rolling Performance Chart

Row

Snail Trail Chart